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International Conference on Mechatronics and Intelligent Materials

作者： Li, Ming Ji, Xueling SW Forestry Univ Coll Commun Machinery & Civil Engn Kunming 650224 Peoples R China

ISBN: (纸本)9783037850695

The loss of the population diversity leads to the premature convergence in existing particle swarm optimization (pso) algorithm. In order to solve this problem, a novel version of pso algorithm called bacterial pso (Bacpso), was proposed in this paper. In the new algorithm, the individuals were replaced by bacterial, and a new evolutionary mechanism was designed by the basic law of evolution of bacterial colony. Such evolutionary mechanism also generated a new natural termination criterion. Propagation and death operators were used to keep the population diversity of Bacpso. The simulation results show that Bacpso algorithm not only significantly improves convergence speed, but also can converge to the global optimum.

关键词： pso algorithm bacterial individual evolutionary mechanism global optimum

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A Dynamic Neighbourhood Particle Swarm Optimisation algorithm for Constrained Optimisation

A Dynamic Neighbourhood Particle Swarm Optimisation Algorith...

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ICELIE/IES Industry Forum/37th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)

作者： Li, Lily D. Yu, Xinghuo Li, Xiaodong Guo, William School of School of Information and Communication Technology CQ University Australia

ISBN: (纸本)9781612849720

This paper presents a dynamic neighbourhood Particle Swarm Optimisation (pso) algorithm for constrained optimisation problems. The constraint dominance concept is adopted for constraint handling. The dynamic neighbourhood aims to enhance communication between particles from different groups in order to improve searching performance. Simulation to the benchmark functions demonstrates the proposed dynamic neighbourhood pso outperforms the static neighbourhood pso in finding consistent quality results.

关键词： pso algorithm Constrained Optimisation Evolutionary Computation

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A new approach based on pso algorithm to find good computational encoding sequences

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Progress in Natural Science:Materials International 2007年第6期17卷 712-716页

作者： Cui Guangzhao1, Niu Yunyun1, Wang Yanfeng1, Zhang Xuncai2 and Pan Linqiang2(1. School of Electrical and Electronic Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China 2. Research Institute of Biomolecular Computer, Huazhong University of Science and Technology, Wuhan 430074, China) [a]School of Electrical and Electronic Engineering Zhengzhou University of Light Industry Zhengzhou China [b]Research Institute of Biomolecular Computer Huazhong University of Scienc and Technology Wuhan China

Computational encoding DNA sequence design is one of the most important steps in molecular computation. A lot of research work has been done to design reliable sequence library. A revised method based on the support system developed by Tanaka et al. is proposed here with different criteria to construct fitness function. Then we adapt particle swarm optimization (pso) algorithm to our encoding problem. By using the new algorithm, a set of sequences with good quality is generated. The result also shows that our pso-based approach could rapidly converge at the minimum level for an output of the simulation model. The celerity of the algorithm fits our requirements.

关键词： DNA computing computational encoding DNA sequences pso algorithm fitness function

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Study on Risk Dispatch Model of Hydropower Station in the Market Environment

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Electricity 2011年第4期22卷 38-40页

作者： Duan Jinchang and Ding Jie State Grid Electric Power Research Institute Slate Grid Electric Power Research Institute

In the power market environment, due to the uncertainty of the reservoir inflow and the pool purchase price, it is very important to research power generation risk dispatch of hydropower plants, taking into consideration the benefits and risk control of both sides. This paper investigates power generation risk dispatch of hydropower plants in the market environment, and proposes a mathematical model which considers maximization of benefits and risk control, reflects control willingness of risk and benefits, resolves it with the pso algorithm, finding more economic and reasonable results. The feasibility and validity of the model and resolving methods are verified by an example.

关键词： hydropower station optimal dispatch risk dispatch model pso algorithm

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MTSP based solution for minimum mobile node number problem in sweep converge of wireless sensor network

MTSP based solution for minimum mobile node number problem i...

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2011 International Conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

作者： Qiansheng Fang Yan Chen Zhenya Zhang Hongmei Cheng Key Laboratory of Intelligent Building Anhui University of Architecture Hefei China School of Management Anhui University of Architecture Hefei China

Environment physical parameters about the energy efficiency status in building can be monitored with wireless sensor network with mobile node. To reduce the cost and complexity of wireless sensor network, sweep coverage for wireless sensor network with mobile node is defined in this paper. To minimize the number of mobile nodes in desired wireless sensor network, the problem for minimum number of mobile nodes and a fast solution based on MTSP for the problem are presented. In our experiment, the MTSP problem is solved with pso algorithm. Experimental results show that the fast solution based on MTSP can work correctly and quickly.

关键词： sweep converge MTSP pso algorithm

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Liver Cancer Identification Based on pso-SVM Model

Liver Cancer Identification Based on PSO-SVM Model

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11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)

作者： Jiang, Huiyan Tang, Fengzhen Zhang, Xiyue Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9781424478132

This paper proposes a novel liver cancer identification method based on pso-SVM. First, the region of interest (ROI) is determined by Lazy-Snapping, and various texture features are extracted from ROI. Afterwards, F-score algorithm is applied to select relevant features, based on which liver cancer classifier is designed by combining parallel Support Vector Machine (SVM) with Particle Swarm Optimization (pso) algorithm. pso is used to automatically choose parameters for SVM, and the advantage is that it makes the choice of parameter more objective and avoids the randomicity and subjectivity in the traditional SVM whose parameters are decided through trial and error. The experiment results on real-world datasets show that the proposed parallel pso-SVM training algorithm improves the prediction accuracy of liver cancer.

关键词： feature extraction feature selection pso algorithm parallel SVM

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Using Heuristic Rules to Enhance a Multiswarm pso for Dynamic Environments

Using Heuristic Rules to Enhance a Multiswarm PSO for Dynami...

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2010 IEEE World Congress on Computational Intelligence

作者： del Amo, Ignacio G. Pelta, David A. Gonzalez, Juan R. Univ Granada Models Decis & Optimizat MODO Res Grp Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

ISBN: (纸本)9781424481262

The Particle Swarm Optimization (pso) algorithm has been successfully applied to dynamic optimization problems with very competitive results. One of its best performing variants, the mQSO is based on an atomic model, with quantum and trajectory particles. This work introduces a new version of this algorithm which uses heuristic rules for improving its performance. Two new rules are presented: one specifically designed for the mQSO, which locally bursts diversity after a change in the environment, and a second, more general one, which globally increases diversity in a precise way, without disturbing the intensification of the search. The new version with rules is tested against the original one using several variations of the Moving Peaks Benchmark and the Ackley function. The results show a drastic improvement in the performance of the algorithm.

关键词： Ackley function pso algorithm dynamic environments dynamic optimization problems heuristic rules locally bursts diversity mQSO moving peaks benchmark multiswarm pso particle swarm optimisation particle swarm optimization algorithm quantum computing quantum particles trajectory particles

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Energy-balanced unequal clustering protocol for wireless sensor networks

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The Journal of China Universities of Posts and Telecommunications 2010年第4期17卷 94-99页

作者： JIANG Chang-jiang SHI Wei-ren XIANG min TANG Xian-lun College of Automation Chongqing University Chongqing 400044 China College of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 China

Clustering provides an effective way to prolong the lifetime of wireless sensor networks. One of the major issues of a clustering protocol is selecting an optimal group of sensor nodes as the cluster heads to divide the network. Another is the mode of inter-cluster communication. In this paper, an energy-balanced unequal clustering （EBUC） protocol is proposed and evaluated. By using the particle swarm optimization （pso） algorithm, EBUC partitions all nodes into clusters of unequal size, in which the clusters closer to the base station have smaller size. The cluster heads of these clusters can preserve some more energy for the inter-cluster relay traffic and the ＇hot-spots＇ problem can be avoided. For inter-cluster communication, EBUC adopts an energy-aware multihop routing to reduce the energy consumption of the cluster heads. Simulation results demonstrate that the protocol can efficiently decrease the dead speed of the nodes and prolong the network lifetime.

关键词： wireless sensor networks clustering protocol pso algorithm multihop routing

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Corrective action planning considering FACTS allocation and optimal load shedding using bacterial foraging oriented by particle swarm optimization algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2010年第4期18卷 597-612页

作者： Hooshmand, Rahmat Allah Pour, Mostafa Ezatabadi Univ Isfahan Dept Elec Eng Esfahan *** Iran

Reactive power planning (RPP) involves optimal allocation and determination of the type and size of new reactive power (VAR) supplies to satisfy voltage constraints during normal and contingency states. The RPP issue is in fact an optimization of large scale mixed integer nonlinear programming problem, so it is proper to use an evolutionary algorithm to solve the problem. In this paper, in order to solve the RPP problem for corrective action of power systems, the bacterial foraging (BF) oriented by particle swarm optimization (pso) algorithm (BF-pso) is proposed. In the algorithm, the VAR control has been carried out by using flexible AC transmission systems (FACTS) devices, in order to minimize the installation costs of these devices. In order to determine the saving rate in the costs, corrective control is also performed by the utilization of load shedding algorithm. The IEEE 57-Bus system, is used to test the proposed method. The simulation results of the proposed algorithm are compared with pso and genetic algorithms (GA) to show the efficiency of this method in the RPP problem.

关键词： FACTS Devices Reactive Power Planning Load Shedding BF algorithm pso algorithm

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Broadband MVDR Beamformer Applying pso 1

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1st International Conference on Swarm Intelligence

作者： Wang, Liang Song, Zhijie Ocean Univ China Dept Ocean Technol Qingdao 266100 Peoples R China

ISBN: (数字)9783642134951

ISBN: (纸本)9783642134944

In this paper, a broadband MVDR(minimum variance distortionless response) beamforming method based on time-domain (TMVDR) is presented. Using TMVDR beamformer, stable sample matrix estimation could be obtained in short time period. To obtain the stable optimum solution of TMVDR, a numerical searching method optimized by pso algorithm with constrain condition is introduced. Out-sea experiment show the performance of TMVDR beamformer applying pso algorithm proposed in this paper.

关键词： DOA estimation adaptive beamtorming pso algorithm

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